What is S3Model Modeling?

  1. Importance.

    A data collection is the fundamental component of a provable concept.

  2. Permanence and preserved full fidelity.

    Data migrations are costly, both in the process and in information loss. Information loss can mean opportunity loss. Storing data for an unlimited amount of time should be possible with today’s technology.

  3. Contextually derived information.

    Information is data with complete context.

    Complete context in this sense means:

    1. the meaning about an item of data that includes the ontological meaning that shows the relations between the concepts and categories in a subject area or domain.

    2. the spatial information about where the data was collected. In today’s systems, a longitude and latitude is expected.

    3. the temporal aspects such as when the data was collected and any time it was modified as well as any start or stop times that the data can be considered valid.

  4. Semantically-derived knowledge.

    Knowledge is derived from contextually defined information that is managed over a specified time span.

  5. Model flexibility and openness.

    An information provider today cannot know the use cases of information consumers of tomorrow. Therefore, creating models with the complete context that fits all the use cases forever is impossible. In order to fit this reality, models must be defined and accepted as purposeful at the time of creation. In this paradigm, there must be a separation between the structural and semantic aspects of the model.

  6. Managed for sharability and traceability.

    Data models and information instances must be computable, shareable, immutable, traceable and uniquely identifiable. With these capabilities we can reduce errors, improving data quality, as well as reducing the manual data cleaning, improving processing productivity.

  7. Preserved and immutable.

    Proper information modeling must be future-proof, avoiding data migrations. Using proven technologies is essential to insure the future availability of data.


The data precedes the code, and in the model-backed, S3Model world the model precedes the data.

This process is no different in how we currently approach information system design.


  • we think about the data

  • we document the data then

  • we build the application to produce the data.

This approach is the application-centric world.

The difference between the approaches is evident when we go to implementation. In the model-backed, S3Model world:

  • we think about the data

  • we document the data then

  • we build an executable, sharable, structured, semantic model that thoroughly describes the information about a dataset. Then

  • we build applications that produce and process data that meets the requirements of the model.

Now we can build as many varied, purpose-specific applications as needed, knowing that the full meaning of the data is available to each application and not tied up in source code and database structures of the original software application.

In Other Words:

  • Shareable - the models must be shareable across applications. Whether those applications are internal to your enterprise or publically available via the Internet, interoperability is paramount.

  • Structured - fine-grained structural context tells us a lot about the meaning of data. What were the options from which to choose? Was there a minimum or maximum value allowed? What language is used to record the information? These can give us a closed-world view of the constraints.

  • Semantic - what are the semantics of the data? The temporal, ontological and spatial contexts, as well as definitions and open-world constraints, expressed via Semantic Web technologies.

  • Executable - the model must be machine processable using standard, openly available technology.

For more background on these concepts see the Footnotes 1

See the S3Model documentation for details about the underlying technology.

The Future

Now that we can enrich the data in a way that is wholly shareable and machine processable. Data scientists are motivated to continue migrating and improving current algorithms to use semantic web/linked data technologies. This will also lead to new algorithm development with the availability of comprehensive information.

This approach solves the data quality issues that hamper growth of Generalized Artificial Intelligence .